Tonge Image Segmentation And Recognition Research Based On Image Analysis

Tongue inspection is not only main content of four diagnostic methods "observation, auscultation and olfaction, interrogation, palpation" for TCM(Traditional Chinese Medicine), but also one of most important characteristics of TCM diagnostic methods. It has played the significant role during thousand years for Chinese nation. Traditional tongue inspection mainly depends on doctor's eyesight to diagnose a disease. The diagnostic result is not only restricted by doctor's knowledge level and diagnostic experience, but also influenced by external environment such as light and temperature. It lacks of objectivity to be limited for further development. It is the only way to combine TCM expert's clinical experiences with modern information technology to realize quantitative, objective and standard tongue inspection.Original tongue image captured by digital camera under standard light source situation usually contains tongue body, upper lip, partial lower lip and face. Because the color of tongue body and background is similar and no obvious boundary is existed beween tongue body and background, automatic segmentation is difficult. The Original image was converted to HSI space. It was segmented by threshold value of hue and intensity component before it was converted to binary image. Then the sequential algorithm was used to quickly mark connected area and morphologic method was applied to fill little holes in tongue area. Experimental result shows it is a fast algorithm with good segmentation effect. For cases that the white coating covered entire surface of tongue or tongue coating texture was putrid or greasy, spatial information was considered to construct similar criterion and the automatic segmentation of tongue image with tongue coating performed well.Among tongue image features, the color of tongue body and tongue coating and the distribution are main evidences for tongue inspection. According to characteristic of tongue image, it was transformed to HSI space from RGB space and smooth processing was carried for the hue histogram. Then the number of color and initial values of color centers were automatically determined. So standard FCM algorithm was modified and applied to separate the tongue body and tongue coating. Experiments indicate that this algorithm speeds up the clustering iteration, reduces the system operation expenses and enhances the algorithm usability. Compared with method of threshold value segmentation, it can obtain better separation effect of tongue body and tongue coating.On the basis of above result, a fast approach named as CISHF is presented to segment color image. Color image was transformed from RGB space to HSI space firstly. Then rough segmentation was done by threshold value of saturation and intensity to eliminate the noise. Finally hue data was clustered by fuzzy c-means. The formula was revised to calculate the distance from sample data to the cluster center according to characteristics of hue data. The weight of effective hue value was calculated to speed up the cluster process. Experiments show that the performance of the presented algorithm is higher than standard FCM method and better segmentation effect can be obtained.On the basis of separation of tongue body and tongue coating, recognition expression was constructed according to distribution regulation of tongue body and tongue coating and good recognition rate was obtained.